Event detection using inquiries

ABSTRACT

Inquiry data from one or more sources (e.g., client devices) may be analyzed to determine if key terms, date terms, and locality terms are indicative of an event to occur at a locality during one or more dates. Events that are detected may be communicated (e.g., via an electronic message(s)). An owner of a property may receive the electronic message(s) that are communicated for detected events and the owner may act to garner interest in stays at their property. Travelers searching for a property to stay at during the event may receive the electronic message(s) in the form of an offer (e.g., an email, a text message, a Tweet, a newsletter, etc.). The inquiry data may be received in real time and/or may be accessed from a data store. The Inquiry data may be curated to remove non-essential information and/or to include edited key terms, date terms, and locality terms.

FIELD

The present application relates generally to systems, software,electronic messaging, and electronic commerce. More specifically,systems, methods and software to detect events using inquiries aredisclosed.

BACKGROUND

An owner (e.g., an actual owner or agent acting on behalf of the actualowner) of a res, such as a property for lease, sale or rent, may notalways be aware of interest in their property (e.g., by a potentialrenter or buyer) that may arise due to an event that may occur in avicinity in which their property is located. In some cases, an owner maybe a person who is very busy and may not have the time and/or theresources to constantly monitor various forms of communication and/orinformation to obtain data they may use in making a determination as towhether or not a demand exists (e.g., now or at a future time) for atransaction related to their property (e.g., a buyer wanting to lease,sale or rent the property), where the demand may be driven by an eventthat is geographically local to the owner's property (e.g., in the samecity, town, resort, zip code, street, county, or in the vicinity, etc.).

Thus, there is a need for systems, methods and software thatautomatically detects events using electronic information.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments or examples (“examples”) of the present applicationare disclosed in the following detailed description and the accompanyingdrawings. The drawings are not necessarily to scale:

FIG. 1 depicts one example of flow diagram for event detection usinginquiries;

FIG. 2 depicts one example of a computer system;

FIG. 3A depicts one example of a graph of key term density andbackground noise of inquiry data versus time;

FIG. 3B depicts another example of a graph of key term density andbackground noise of inquiry data versus time;

FIG. 4 depicts one example of a backend system to detect events usinginquires and communication between the backend system and other systems;

FIG. 5 depicts one example of a block diagram of an application todetect events using inquiries; and

FIG. 6 depicts an example of locality of event detection using inquires.

DETAILED DESCRIPTION

Various embodiments or examples may be implemented in numerous ways,including as a system, a process, a method, an apparatus, a userinterface, or a series of program instructions on a non-transitorycomputer readable medium such as a computer readable storage medium or acomputer network where the program instructions are sent over optical,electronic, or wireless communication links. In general, operations ofdisclosed processes may be performed in an arbitrary order, unlessotherwise provided in the claims.

A detailed description of one or more examples is provided below alongwith accompanying figures. The detailed description is provided inconnection with such examples, but is not limited to any particularexample. The scope is limited only by the claims and numerousalternatives, modifications, and equivalents are encompassed. Numerousspecific details are set forth in the following description in order toprovide a thorough understanding. These details are provided for thepurpose of example and the described techniques may be practicedaccording to the claims without some or all of these specific details.For clarity, technical material that is known in the technical fieldsrelated to the examples has not been described in detail to avoidunnecessarily obscuring the description.

FIG. 1 depicts one example of flow diagram 100 for event detection usinginquiries. At a stage 102 inquiry data may be accessed from one or moredata sources (e.g., a data store, data base (DB), Cloud storage, theInternet, RAID, NAS, hard drive, SSD, etc.). As one example, a datastore 101 may include inquires in one or more forms or formats, such aselectronic messages, curated inquiries or others. Inquiry data that isaccessed at the stage 102 may include but is not limited to free text,structured text, or both or any other text based or non-text basedformats, for example. Free text may include but is not limited to textor other characters or symbols entered by a user using one or more userinput devices such as a digit(s) of a hand(s), voice input, handwriting, a keyboard, stylus, touch screen, keypad, or mouse, forexample. Free text may be included in an electronic message and may beincluded in one or more portions of the electronic message (EM)including but not limited to a subject line of the EM, a body of the EM,a notes field of the EM, a comments field of the EM, a header of the EM,and a data payload of the EM, just to name a few. Example EM's includebut are not limited to email, text messages, Short Message Service(SMS), push messages, push notifications, instant messages (IM), Tweets,EM's from web sites, web pages, the Internet, the Cloud, voice to textconversion, electronic forms, data entered into a menu, a form, just toname a few. Structured text may include but is not limited to drop downmenus on a GUI, an APP (e.g. a mobile APP on a smartphone or tablet) ora dashboard, a check box in a menu, a radio button, a fillable form, anicon selected or otherwise activated on a screen or display of a device,data entered on a web page or web site, etc.

According to some examples, inquiry data may include textual or otherforms of data that may be communicated by one party to another party,such as a user (e.g., a traveler or customer) to an owner of a propertythe traveler is inquiring about (e.g., location, price, availability fora stay date range, etc.). Inquiry data may include comments, notes,queries, questions, statements or other text that may provide a nexusbetween one or more of location of the property, the stay dates,availability, price, and the like, for example. In some examples, anevent that may occur near the location of the property during someportion of the stay date may be described in the text and/or other datain the inquiry.

At a stage 104 a determination may be made as to whether or not toremove (e.g., filter out) stock terms that may be included in theinquiry data. Stock terms may include but are not limited to standardterms or commonly used terms that may be included in an inquiry (e.g.,as free text and/or structured text), such as words like: “property”;“rent”; “lease”; “no pets”; “no smoking”; “condo”; “house”; “bedroom”;“hotel”; “apartment”; “bathroom”; “motel”; “parking”; “kitchen”;“garage”; “pool”; and “Jacuzzi” may be removed from the inquiry data. Asone example, the inquiry data may be parsed to detect (e.g., usinglexical analysis) and to extract stock terms from the inquiry data. If aYES branch is taken from the stage 104, then at a stage 106 the inquirydata may be parsed (e.g., analyzed) to remove stock terms and flow 100may subsequently resume at another stage, such as a stage 108, forexample. On the other hand, if a NO branch is taken from the stage 106,then flow 100 may continue at another stage, such as the stage 108, forexample. Stock terms to be removed at the stage 106 may be included in adata base, hash table, lookup table, dictionary, data stored or otherlocation. Stock terms may be dependent on locality (e.g., a specificregion, state, country, zip code, postal code, etc.), language (e.g.,French, Spanish, Mandarin, Russian, etc.), and stock terms may changeover time and may be added to with new stock terms or may be reduced bydeleting stock terms.

At the stage 108, date terms may be extracted from the inquiry data.Date terms may include free text, structured text or both. Date termsmay include stay dates for traveler's stay at a property. Date terms mayinclude text and/or data that define a beginning stay date (e.g., anarrival date) and an end stay date (e.g., a departure date), a certainnumber of days and nights of a stay (e.g., three days and two nightsbeginning on a certain day and ending on a certain day, etc.). Althoughdate terms may be regarded as a type of stock term, date terms may beused at other stages in flow 100 and may not be removed at the stage 106as described above.

At a stage 110, key terms may be extracted from the inquiry data. Keyterms may include free text, structured text or both. One or more keyterms may be used to associate an event or events with the need for aplace to stay during the event(s). In some examples, key terms may mostlikely comprise free text that may be entered by a user (e.g., atraveler or customer) in an electronic message (EM) (e.g., an email,text, IM, SMS, or Tweet, etc.), a graphical users interface (GUI), adash board, in an application (APP) (e.g., on a smartphone, tablet orthe like), an application program interface (API), or some other medium.Examples of key terms that may be included in inquiry data may includebut are not limited to “South by Southwest”, “SXSW”, “Match”, “NewportJazz Festival”, “Contest”, “Film Festival”, “Festival”, “Tournament”,“Championship”, “Bowl”, “Concert”, “F1”, “Grand Prix”, “Olympics”,“Race”, etc., just to name a few. Other examples may include but are notlimited to names of famous persons, groups, performers, bands, athletes,coaches, performances, entertainers, speaker, authors, politicians,actors and others that may be associated with an event(s).

At a stage 112, one or more locality terms may be extracted from theinquiry data. A locality term may typically constitute a term or termsthat identify a location of an event, a location of the property orboth. For example, locality terms for a city in the state of Missouri inthe United States of America may be “Missouri” for the state and“Branson” for the city located in Missouri. As another example, thelocality terms may include but are not limited to “USA”, “Missouri”,“MO”, “Ozark Mountains”, a zip code(s) such as “95615” or “95616”, and“Branson”. In some example a locality term may be of a secondarylocation that is near a primary location, such as a suburb of a majorcity. In some examples, the primary location may be the site for adetected event. In other examples, the secondary location may be thesite for a detected event. In some examples a locality term may beassociated with a location that is within a certain distance (e.g.,within a 15 mile radius) from an event and/or another location. In theabove example of Branson, Mo., there may be several cities nearbyBranson (e.g., 22 cities within a 15 mile radius) one or more of thosecities may appear as a locality term in the inquiry data. For example,cities nearby Branson may include “ROCKAWAY BEACH”, “WALNUT SHADE, Mo.”,“HOLLISTER, Missouri”, “OAK GROVE, Ark.” or variations of the foregoing.Typically, a locality term that may be included in inquiry data may beof finer granularity that may be used to more closely associate an eventwith a property near the event, for example. Therefore, the state of“MO” is not as fine grained a determination of a “Bluegrass Festival” tobe held in “Branson” as is a location term that includes the word“Branson” or a nearby city/town, even though the word “Branson” may alsoappear with the word “MO” or “Missouri”, for example. In other examples,a locality term may include a term associated with a venue, park,natural attraction, structure, theater, building or the like. As oneexample, locality terms for Branson, Mo. may include “Silver DollarCity” as a venue (e.g., a theme park) associated with an event inBranson (e.g., the “Bluegrass and BBQ Festival”). The aforementioneddate terms may be associated with the event, such as dates “Friday, May8, 2015-Monday, Jun. 1, 2015” for the 2015 “Bluegrass and BBQ Festival”at “Silver Dollar City” in “Branson” “Mo.”, for example.

At a stage 114 a determination may be made as to whether or not toaccess additional locality data. If a YES branch is taken from the stage114, then flow 100 may transition to a stage 116 where additionallocality data (e.g., locality terms, GPS data, location tracking data,etc.) may be accessed and extracted. In some example, the stage 116 mayaccess geographical location data (e.g., geolocation data) 103 toextract the additional locality data. The additional locality data mayinclude data from a client device used by the user (e.g., a customer ortraveler), GPS data, location tracking data, or other forms of localitydata. Browser search history, cookies, click through on hyperlinks, webpages visited, IP addresses, EM's transmitted and/or received, cellulartowers accessed by a client device, and other forms of data that may begarnered from various systems, devices, and media may be included in theadditional locality data. Stage 116 may transition to a stage 118 afterthe additional locality data has been accessed and extracted. If a NObranch is taken from the stage 114, then flow 100 may transition toanother stage, such as the stage 118.

At the stage 118, terms extracted from one or more of the prior stages(e.g., 108, 110, 112, 116) may be analyzed. A semantic analysis tool maybe used to analyze the extracted terms for terms related to locality(e.g., in a region around a town, a county, a city, etc.), dates for keyterms such as events or other activities that may be associated with thelocality and/or date terms. For example, analysis of extracted terms atthe stage 118 may include extracting from inquiry data “Austin, Tex.” aslocality terms, dates “Oct. 31, 2014 to Nov. 2, 2014” as date terms, and“UNITED STATES GRAND PRIX” as a key term. Other extracted terms from theinquiry data may be key terms or locality terms. For example, “Circuitof The Americas” may be a locality term that specifies a venue (e.g., aF1 race track) for an event and “Austin F1 GP” may be a key term thatspecifies an event (e.g., an F1 Grand Prix race to be held in Austin,Tex.). In the above example, a traveler may have include those terms inan EM or other communication that may be transmitted or otherwisecommunicated to an owner or an agent for the owner as in inquiry forobtaining a place to stay at during the event (e.g., a place in or nearAustin, Tex. for the F1 GP race).

At a stage 120 a density of key terms may be determined using one ormore of the date terms, the locality terms or both. In flow 100, theremay be many received inquiries 101 (e.g., from an inquiry feed) that aremade by many travelers and communicated to many owners. On a localitybasis (e.g., in Austin, Tex. or in Branson, Mo.) a portion of theinquiries for those location may include key terms such as “Bluegrassand BBQ Festival” for the Branson, Mo. locality or “Austin F1 GP” forthe Austin, Tex. locality.

At a stage 122 the density of key terms may be compared with a baselinedensity of inquiry data (e.g., a background noise of inquiry data for aparticular locality such as Austin, Tex. or Branson, Mo.). The baselinedensity may include inquires that do not include key terms. For example,the baseline density of inquiries may be from travelers looking forplaces to stay that are unrelated to any specific events in thelocality. As another example, the baseline density of inquiries mayinclude industry specific stock terms such as “property”, “rent”,“rental”, “lease”, “bedrooms”, “bathrooms”, “kitchen”, “parking”,“price”, “rate”, etc., that may be parsed, analyzed and subsequently bedisregarded as not being related to one or more events. The comparisonof the density of key terms with the baseline density of inquiry datamay be used to determine if the density of key terms is higher than thebaseline density of inquiry data.

Statistical analysis or other analytical methods may be used to performthe comparison. The statistical analysis or other analytical methods maybe implemented in hardware (e.g., a hardware accelerator or circuitry),software (e.g., algorithms, API, etc.) or both. A higher density for thekey terms may indicate an event in a locality over a data range. Forexample, statistical analysis may include but is not limited to applyingtext analytics (e.g., text mining, text data mining), predictiveanalytics, sentiment analysis, or other analytical tool(s), to thedensity of key terms and the baseline density of inquiry data to detectpatterns in the density of key terms (e.g., irregular patterns) that arenot detected in the baseline density of inquiry data (e.g., regularpatterns). As another example, statistical analysis may include applyingstatistical pattern learning to the detected patterns to extract datarepresenting one or more of clustered text, relevance of text, noveltyof text, categorization of text, or sentiment of text, for example. Afrequency of word distribution may be analyzed to associate text withevents in a locality over a date range, for example. In some examples,in a locality during a data range, received inquiry data (e.g., emails,HTML, Tweets, SMS, text messages, etc.) may be analyzed to determine thenumber of times one or more key terms appears in the inquiry data (e.g.,the number of times text “F1”, “Grand Prix”, “race”, etc. occur in theinquiry data), the number of times the one or more key terms appears inother inquiry data, and optionally, the length of the inquiry data(e.g., emails, HTML, text, Tweets may have different document lengthsand/or sizes in bytes). The occurrence of the key terms (e.g., “F1”,“Grand Prix”, “race”) may be compared to the baseline density of inquirydata to detect a pattern of inquires that are unrelated to backgroundnoise associated with the baseline density of inquiry data. Machinelearning may be applied to the detected pattern to determine if detectedkey terms are associated with an event that historically has occurred inthe selected locality during the date range, or may constitute a newevent that may over time be determined (e.g., based on machine learning)to be a one-time event, an occasional event (e.g., not regularlyoccurring), or a regularly recurring event (e.g., occurs during thesecond week of May each year), for example.

As another example, of statistical analysis may include determining afrequency of key terms as a function of inverse document frequency(e.g., term frequency-inverse document frequency (tf-idf) orcosine-normalized tf-idf), where each inquiry may constitute a documentthat may include one or more key terms (e.g., although some inquires maynot include any key terms). Key terms may be assigned a weight based ontheir frequency of occurrence in multiple inquires for a given datarange. A weighting factor may be applied to determine which key terms tosurface as being most relevant to and/or indicative of an event. Forexample, key terms may be weighted proportional to key terms frequency(e.g., key terms having higher frequencies are assigned higher weightingfactors).

For example, each inquiry may be parsed or otherwise analyzed to detectkey terms and determine a key term frequency of the key term in theinquiry (e.g., the number of instances of the key term in the inquiry).An inverse inquiry frequency may be determined based on the number ofinquiries out of the total number of inquiries that include the keyterm. A product of the key term frequency and the inverse inquiryfrequency may be calculated (e.g., computed on a compute engine).Optionally, a key term weighting factor may be applied to the product ofthe key term frequency and the inverse inquiry frequency. Key termshaving higher key term frequencies may be assigned a higher key termweighting factor, for example.

At a stage 124 a determination may be made as to whether or not thecomparison at the stage 122 detected an event or events. If a NO branchis taken from the stage 124, then flow 100 may transition to anotherstage, such as back to the stage 102, for example. If a YES branch istaken from the stage 124, then flow 100 may transition to a stage 126.

At the stage 126 a determination may be made as to whether or not theevent or events that were detected are known events. A known event mayinclude events that have a history of occurring in the locality (e.g.,annually or in some other interval). Known events for a given localitymay be included in a data store for events data 115, for example. Eventsdata 115 may be accessed at the stage 126 to determine if the event orevents that were detected are included in the events data 115. The sameevent may be known by different names and/or acronyms, such as “SXSW”for “South by Southwest”, for example. Events data 115 may includemultiple data entries for different variations for event names. Otherdata sources (e.g., external or internal sources) may be accessed todetermine if an event is known, such as one or more networks 105 (e.g.,an external resource, a data repository, the Internet, a Cloud basedresource, etc.), for example. Network 105 may include networkedresources such as compute engines (e.g., servers, processors, etc.),data stores (e.g., RAID, NAS, data base, etc.), and communicationsnetworks (e.g., a wired 105 a and/or a wireless 105 b communicationsnetwork). A search engine or the like may be used to access the network105 to determine if a an event name used as the search string turns upone or more hits or matches for the event. If the event is a knownevent, then a YES branch may be taken from the stage 126 and flow 100may transition to a stage 130.

If the event or events are not known, then a NO branch may be taken fromthe stage 126 and flow 100 may transition to a stage 128. At the stage128, a machine search (e.g., via machine learning, artificialintelligence, a search engine, a data base), a curated search or bothmay be used to find information related to an unknown event. In someexamples, the machine search may include using network(s) 105 asdescribed above. In other examples, the curated search may include usingdata generated responsive to manual input to perform curated research107. The curated research 107 may constitute use of electronic resources111 and/or hard copy/textual 113 resources to discover information aboutthe event or events. The curated research 107 may communicate (e.g.,with external resources such as network 105, etc.) using wired and/orwireless communications 107 a. At the stage 128, the results of thecurated research 107 may be added to the events data 115 so that infuture iterations of flow 100 the event(s) will be known events and theYES branch may be taken from the stage 126.

At the stage 130, event notifications, promotions, and other forms ofelectronic messages may be communicated to owners of properties in thelocality of the events. For example, owners of property listings (e.g.,for lease, sale, or rent) in the Austin, Tex. locality may be apprisedof a high density of key terms that have been surfacing in receivedinquiry data that indicates an interest by travelers in obtaining staydates in Austin, Tex. for one or more detected events, such as the SXSWfestival and the F1 GP race at the Circuit of The Americas. Some of theproperty owners may be unaware of those events and/or a demand for staysat properties in the localities those events occur in. To that end, atthe stage 130, the communications may be used to inform the owners andput the owners on notice of a potential to reach out to travelers and/oraccept offers from travelers in need of stay date.

Commutation of event notifications and/or promotions may include but isnot limited to: electronic messages sent to owners in the form ofemails, newsletters, text messages, SMS, Tweets, push messages, pushnotifications, instant messages, Tweets, or other forms of electronicmessaging; advising owners to initiate a search engine optimization(SEO) for their properties or change a SEO mix; acting on behalf ofowners to initiate and/or change a SEO; generating ads in print/hardcopy media that promote the owner properties in the locality of theevent; generating ads in electronic media that promote the ownerproperties in the locality of the event (e.g., in browsers, APP's,emails, Craigslist, eBay, Zillow, etc.); and communicating electronicmessages to travelers whose inquiries are related to the event(s) in thelocality, where the electronic message may include available ownerproperties for the stay dates the traveler included in the inquiry data.

After the stage 130 has completed, flow 100 may transition to anotherstage, such as back to the stage 102, for example. Flow 100 mayrepeatedly cycle through one or more of its various stages to processkey terms, date terms and locality terms for events occurring over manylocalities in different states, counties, countries, etc.

FIG. 2 depicts one example of a computer system 200. In FIG. 2, computersystem 200 may be suitable for use in one or more systems, devices,compute engines, apparatus, client devices, wireless devices, wirelesssystems, backend systems, front end systems, host devices or othersdescribed in reference to FIGS. 1 and 3A-6. In some examples, computersystem 200 may be used to implement computer programs, algorithms,applications, configurations, methods, processes, or other software toperform the above-described techniques. Computer system 200 includes abus 202 or other communication mechanism for communicating information,which interconnects subsystems and devices, such as one or moreprocessors 204 (e.g., μC, μP, DSP, ASIC, FPGA, Baseband, etc.), systemmemory 206 (e.g., RAM, SRAM, DRAM, Flash), storage device 208 (e.g.,Flash, ROM), disk drive 210 (e.g., magnetic, optical, solid state),communication interface 212 (e.g., modem, Ethernet, WiFi, Cellular),display 214 (e.g., CRT, LCD, LED, OLED, touch screen), input device 216(e.g., keyboard, stylus, touch screen, mouse, track pad), and cursorcontrol 218 (e.g., mouse, trackball, stylus). Some of the elementsdepicted in computer system 200 may be optional, such as elements214-218, for example and computer system 200 need not include all of theelements depicted.

According to some examples, computer system 200 performs specificoperations by processor 204 executing one or more sequences of one ormore instructions stored in system memory 206. Such instructions may beread into system memory 206 from another non-transitory computerreadable medium, such as storage device 208 or disk drive 210 (e.g., aHDD or SSD). In some examples, circuitry may be used in place of or incombination with software instructions for implementation. For example,circuitry (e.g., circuitry, an ASIC, a FPGA, logic gates, one or moreprocessors, etc.) may be used to perform hardware acceleration (e.g.,for statistical analysis or other type of analysis at stage 122 of flow100). A high level description language such as VHDL, Verilog, Synopsysor the like may be used to synthesize circuitry that implements thehardware accelerator. The term “non-transitory computer readable medium”refers to any tangible medium that participates in providinginstructions to processor 204 for execution. Such a medium may take manyforms, including but not limited to, non-volatile media and volatilemedia. Non-volatile media includes, for example, optical, magnetic, orsolid state disks, such as disk drive 210. Volatile media includesdynamic memory, such as system memory 206. Common forms ofnon-transitory computer readable media includes, for example, floppydisk, flexible disk, hard disk, SSD, magnetic tape, any other magneticmedium, CD-ROM, DVD-ROM, Blu-Ray ROM, USB thumb drive, SD Card, anyother optical medium, punch cards, paper tape, any other physical mediumwith patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memorychip or cartridge, or any other medium from which a computer may read.

Instructions may further be transmitted or received using a transmissionmedium. The term “transmission medium” may include any tangible orintangible medium that is capable of storing, encoding or carryinginstructions for execution by the machine, and includes digital oranalog communications signals or other intangible medium to facilitatecommunication of such instructions. Transmission media includes coaxialcables, copper wire, and fiber optics, including wires that comprise bus202 for transmitting a computer data signal. In some examples, executionof the sequences of instructions may be performed by a single computersystem 200. According to some examples, two or more computer systems 200coupled by communication link 220 (e.g., LAN, Ethernet, PSTN, orwireless network) may perform the sequence of instructions incoordination with one another. Computer system 200 may transmit andreceive messages, data, and instructions, including programs, (i.e.,application code), through communication link 220 and communicationinterface 212. Received program code may be executed by processor 204 asit is received, and/or stored in disk drive 210, or other non-volatilestorage for later execution. Computer system 200 may optionally includea wireless transceiver 213 in communication with the communicationinterface 212 and coupled 215 with an antenna 217 for receiving andgenerating RF signals 221, such as from a WiFi network, WiMAX network,BT radio, Cellular network (e.g., 3G, 4G, 5G, etc.), near fieldcommunication (NFC), satellite network, or other wireless network and/orwireless devices, for example. Examples of wireless devices (e.g.,client devices) may include but is not limited to those depicted in FIG.4 such as one or more of devices 410, 111, 417, 423, 455, and 422.

FIG. 3A depicts one example of a graph 300 of key term density andbackground noise of inquiry data versus time. In FIG. 3A on an x-axis ofgraph 300, time (t) is represented (e.g., in days, weeks, months, orother units, etc.). Here, time t may be represented in increments ofweeks and x-axis may represent a length or period of time t (e.g., suchas ≥90 days). A y-axis of graph 300 may represent a density (ρ) of keyterms that have been extracted from inquiry data as described above inreference to flow 100 of FIG. 1.

A first event that may have been detected from inquiry data (ID) usingflow 100 is denoted as SXSW ID 302 for key terms related to the South bySouthwest music festival held in Austin, Tex., typically starting aroundthe second week of March through the third week in March. For example,in the year 2014, SXSW may take place from March 7 through March 16. Onthe x-axis for the weeks of March 2014, density of key terms for SXSW ID302 are depicted as spanning from the 2^(nd) week to the 3^(rd) week ofMarch 2014. Graph 300 also depicts a background noise density of inquirydata denoted as BKN ID 301. A density for BKN ID 301 may be related toinquiries (e.g., inquires unrelated to SXSW) for stay dates in Austin,Tex. in general as may be the case for business travelers, travelersvisiting family in the Austin area, travelers visiting the Austin areafor reasons other than SXSW, etc. Here, BKN ID 301 approximately spans adensity range from below 0.1 to slightly above 0.2; whereas, the densityof terms for SXSW ID 302 approximately spans a density range from about0.35 to about 0.62 in the date range spanning from the 2^(nd) week tothe 3^(rd) week of March 2014. The BKN ID 301 for all four weeks ofMarch 2014 is approximately the same. Therefore, comparison (e.g., usingtext analytics or other forms of statistical analysis) of the lowerdensity for BKN ID 301 with the higher density for SXSW ID 302 may beindicative of key terms surfaced from received inquiry data (e.g., 101of FIG. 1) that an event of interest to travelers will occur during the2^(nd) and 3^(rd) weeks of March 2014 and notifications/promotionsregarding listings available for stay dates in the 2^(nd) and 3^(rd)weeks of March 2014 may be communicated to owner, traveler or others asdescribed above.

FIG. 3B depicts another example of a graph 350 of key term density andbackground noise of inquiry data versus time. Graph 350 depicts examplesof key term densities vs. back ground noise inquiry data for F1 Track ID304 in Austin, Tex. and for Cannes FF ID 306 in Cannes, France, andtheir associated back ground noise inquiry data BKN ID 303 and BKN ID305, respectively. In graph 350, the depicted times (e.g., weeks in amonth) are beyond the March 2014 times described above in reference tograph 300 of FIG. 3A are depicted. Approximately from the 1^(st) throughthe 3^(rd) weeks of May 2014, received inquiry data (e.g., 101 ofFIG. 1) may include key terms for a Formula 1 Grand Prix racing event tobe held at the Circuit of The Americas in Austin, Tex. may surface inthe inquiry data. The density of key terms F1 Track ID 304 when comparedto the back ground noise inquiry data BKN ID 303 (e.g., for the month ofMay 2014) may indicate an important event that may be advantageous forowners to act upon. The BKN ID 303 may be different than the back groundnoise inquiry data for other periods of time on the x-axis, such as theback ground noise inquiry data for May 2014 being different than theback ground noise inquiry data BKN ID 301 (in graph 300 of FIG. 3A) forMarch 2014, for example. Although events SWSX and F1 Track, in FIG. 3Aand FIG. 3B, respectively, both occur in the same locality (e.g.,Austin, Tex.), events that are detected may be geographically diverse.For example, graph 350 depicts the Cannes Film Festival event Cannes FFID 306 that occurs in Cannes, France. Here, comparison between thedensity of key terms for Cannes FF ID 306 vs. the back ground noiseinquiry data BKN ID 305 in 2^(nd) through 4^(th) weeks of May 2014 mayindicate a high density of inquiries for stay dates in or around thevicinity of Cannes, France for travelers wishing to attend the filmfestival. Although for purposes of explanation the inquiry data 307 forthe Cannes FF ID 306 and BKN ID 305 are depicted on the graph 350 alongwith events that occur in Austin, Tex. (e.g., F1 Track ID 304), eachevent may be represented on its own graph.

The comparison of the density of key terms ρ with the back ground noiseinquiry data BKN ID may include different amounts of inquiry data. Forexample, the back ground noise inquiry data BKN ID may be derived fromseveral hundred to several thousand or more inquires that did notsurface any key terms and those inquires may only have included stockterms. On the other hand, the inquiry data for the density of key termsρ may include more inquiries or less inquiries than the back groundnoise inquiry data BKN ID. As another example, for a large event, suchas a Winter or Summer Olympics where there may be a large number oftravelers seeking stay dates in the locality of the games, the densityof key terms ρ may include more inquiries than the back ground noiseinquiry data BKN ID due to demand for stay dates being driven by theOlympic event. As another example, for an event such as the San DiegoQuilt Show 2014, to be held in San Diego, Calif. from September4^(th)-6^(th) at the San Diego Convention Center, the back ground noiseinquiry data BKN ID may include more inquiry data due to other inquiriesfor the San Diego locality that may be more related to vacation andtourist attractions in San Diego. However, the key terms surfaced for“Quilt Show”, “Quilting Bee”, “San Diego Convention Center”, etc. may besufficient to detect an event that may be of interest to owners,travelers, and the like.

FIG. 4 depicts one example 400 of a backend system to detect eventsusing inquires and communication between the backend system and othersystems. In example 400, a backend system 450 may be configured todetect events using inquires and communication between the backendsystem 450 and other systems (e.g., 410, 422, 423, 107, 415, 105, etc.)is depicted. One or more of the backend systems 450 may include(internally and/or externally) compute resource 451 (e.g., a server orthe like), data storage 453 (e.g., RAID or Cloud storage), one or moredata stores for received inquiries 101, Geolocation Data 103 and eventsdata 115, a firewall 457 for data/network security, a communicationaccess point 455 (e.g., a wired 456 and/or wireless 421 communications),and an application 452 (e.g., a non-transitory computer readable medium)that may execute on compute resource 451 and/or other compute engines.The exemplary computer system 200 of FIG. 2 may be used to implementcompute resource 451 and may also be used to implement other devices andsystems depicted in FIG. 4.

Other systems and resources that may communicate 421 (e.g., via wirelessand/or wired links) with backend system 450 include but are not limitedto network 105, Cellular network 422, wireless network 423, customerservice 415, curated research 107, one or more client devices 410, anduser 412 (e.g., via client device 410 in communication 421 with customerservice 415). Inquires that are received into received inquiries 101 andoptionally GEO/Location data stored in Geolocation Data 103 that may begenerated by client devices 410 or activities on client devices 410(e.g., web browsing, use of search engines, wireless network access,metadata generated by activity on the client device, click trough's onads or hyperlinks, metadata included in images, media, video, audio,etc.) may take numerous forms including but not limited to electronicmessages, emails, outputs from APP's, GUI's, Dashboards, and website/web page activity on client devices 410. The one or more clientdevices 410 that are depicted in FIG. 4 may be devices from differentmanufactures, may run different operating systems (OS), and maycommunicate 421 using different communications protocols. Inquiries maybe entered or otherwise input on a client device 410 using a voiceinterface, gesture recognition, stylus, finger, keyboard, touch screen,or other forms of user interface.

As one example, a traveler (e.g., a customer or buyer) may use atablet/pad client device 410 to communicate 421 with a web site (e.g.,network 105) that displays on a screen of the device 410, structuredtext 423 for items such as a drop down menu, one or more check boxes forselecting a property type, such as a “Condo”, a “House”, or an“Apartment” and other preferences such as a property which enforces orguarantees a “Non-Smoking” room and/or environment for its guests. Inexample 400, the traveler has selected a “Non-Smoking”, “House” or“Condo” as preferences for the stay. The drop down menu may be used bythe traveler to select other items such a payment method (e.g., VISA,MasterCard, PayPal, American Express, Bitcoin, etc.). Optionally oralternatively, the traveler may enter free text 442 in a field, screen,box, window, or other area presented on a display of client device 410.Here, the free text 442 may comprise one or more of the key terms usedfor detecting an event that may occur in a location, such as free textfor “SXSW, Austin, Tex., Mar. 6-Mar. 18, 2014, 2bd, 2ba, Property”.Here, stock terms in the free text 442 such as “Property”, “2bd”, and“2ba” may be removed or otherwise filtered out as described above. Otherfree text 442 terms such as stay dates “Mar. 6-Mar. 18, 2014”, locality“Austin, Tex.”, and potential event description “SXSW” may be extractedand processed by backend system 450 along with inquiries from othertravelers to arrive at a high density of key terms indicative of anevent being detected.

Backend system 450 may access received inquiries 101 to compute thedensity of background noise for inquiry data for use in the comparisonwith the density of key terms in making the determination that an actualevent has been detected. Backend system 450 may access events data 115to determine whether or not “SXSW” is an already known event. If theevent is not a known event, backend system 450 may access one or more ofcurated research 107, customer service 415, network 105 or otherresources to divine the meaning of “SXSW” as it relates to an event.

In some examples, user 412 (e.g., a traveler) may use a client device410 (e.g., a phone, a smartphone, a tablet or pad) to contact customerservice 415. Customer service 415 may listen to needs of user 415 forstay dates in Austin, Tex. from “Mar. 6-Mar. 18, 2014” to attend “Southby SouthWest” and the user 412's need for a “2bd, 2ba, Property”.Customer service 415 may enter the data supplied by user 412 as freetext, structured text or both and communicate 421 that data to backendsystem 450.

Other travelers using other client devices 410 may also make inquiriessimilar to the above example, using email, electronic messaging, adashboard, a GUI, or an APP on their client devices 410. As more andmore travelers submit inquiries for the same locality, within a similardate range, for a specific event, the density of key terms for thatevent for the date terms, the locality terms or both may increaserelative to the baseline density of inquiry data. As a result, thecomparison of the density of key terms with the density of baselineinquiry data may indicate an event of interest to many travelers, andmay prompt the generation of communications to owners of properties invicinity of the locality to take action to communicate availability andterms for their respective properties to potential travelers and/ortravelers that have submitted inquiries.

Application 452 may implement one or more stages of flow 100 or otherprocess by which events may be detected (e.g., automatically detected)based on inquiries and the data included in those inquires. Application452 may make one or more calls to other algorithms to obtain data (e.g.,from 101, 103, 115, 105, 107, 415) from various sources and/or datastores. For example, application 452 may make application programminginterface (API) calls. In some examples, APP's or other algorithmsexecuting on client devices 410 or on systems in communications withclient devices 410 may perform one or more stages of flow 100, includingbut not limited to filtering out stock terms at the stage 106,extracting date terms at the stage 108, extracting locality terms at thestage 112, or accessing and extracting additional locality terms at thestage 116, for example.

Backend system 450 may use communication access point 455 to communicate(421, 456) event notifications and/or promotions to owners, agents ofowners, to travelers and other potential customers or clients, forexample. Backend system 450 may be replicated at different geographicallocations to detect events based on inquiries in those differentgeographical locations (e.g., in Europe, Asia, Far East, North America,South America, etc.).

FIG. 5 depicts one example of a block diagram 500 of an application todetect events using inquiries. In block diagram 500, application 452 mayinclude a data collection 510 utility being configured to collect datafrom one or more data stores (e.g., a data base (DB)) such as InquiryDB/Feeds 512 to obtain inquiry data, Geolocation DB 514 to obtainlocation data, and Events DB 516 to obtain data on known events or toadd newly detected events to the DB 516. Application 452 may include alanguage analysis tool 520 to analyze terms associated with dates,locality, and events, for example. Language analysis tool 520 may usesemantic analysis tools and/or algorithms. Application 452 may include astatistical analysis tool 530 being configured to compare the density ofkey terms with the density of baseline inquiry data to determine if thedensity of key terms when compared with the density of baseline inquirydata indicates an event. Application 452 may include an event detectedaction(s) 540 utility being configured to take action, after an event orevents have been detected, to notify/communicate, the event or events,and other relevant data to owners, agents of owners, travelers, andother potential customers or clients. Event detected action(s) 540 maygenerate or cause to be generated, information, electronic messages andother forms of communication including but not limited to search engineoptimization (SEO) 551, information to be presented on an ownerinterface 541, electronic messaging 543 in one or more forms, owner SEOrecommendations 545, ad generation 547 using one or more forms of media,and newsletter generation 549 (e.g., to owners, travelers, or otherinterested parties), for example. As one example, upon detecting the“SXSW” event in Austin, Tex., Event detected action(s) 540 maycommunicate 421 a text message to one or more owners stating that anevent “SXSW” is surfacing in inquiries from travelers for stay dates inAustin, Tex. for the 2^(nd) and 3^(rd) weeks of March 2014. The textmessage may further advise the owners to change their SEO mix or termsto target searches for places to stay in Austin, Tex. during the “SXSW”music festival.

As another example, Event detected action(s) 540 may communicate 421 anelectronic message in the form of a newsletter emailed to owners andhighlighting interest in stay dates for the 2014 “SXSW” music festivalin locations in and/or around Austin, Tex. Other forms of electronicmessages may be communicated 421 to owners, agents of owners, travelersor other interested parties or potential customer and clients, such asTweets, Instant Messaging (IM), SMS, push messages, push notifications,offers or ads included in content displayed in a browser, a web page, anemail application, etc. Event detected action(s) 540 may addresselectronic messages to one or more addresses such as email addresses,web addresses (e.g., Uniform Resource Identifier (URI) or UniformResource Locator (URL)), SMS address (e.g., a cellular phone number), aTwitter handle, a social media address, a professional media address,Internet Protocol (IP) Address, Media Access Control (MAC) Address,physical address, Service Set Identifier (SSID), Bluetooth address,wired and/or wireless network address, or other forms of addressesand/or addressing that may be electronically communicated.

FIG. 6 depicts an example 600 of locality of event detection usinginquires. Events that may occur in a broader geographical jurisdictiondenoted by the dashed circles, such as the United States of America(USA) or a state in the USA, a country in Europe or the European Union(EU), Brazil or Australia may lack sufficient locality and may benarrowed down to smaller geographical areas, denoted by the smallersolid circle within the dashed circles, such as: Park City, Utah;Austin, Tex.; Cannes, France; Melbourne, Australia, or several World CupSoccer sites in various cities of Brazil, for example.

In example 600, inquiry data may surface two events (E1, E2) in localityAustin, Tex., for SXSW music festival and F1-GP auto racing,respectively. Surfaced events (E1, E2) may occur at different a time tand at different locations in or around Austin, Tex., for example.Furthermore, each event (E1, E2) may have a different key term density ρvs. time t graph. Background (BKN ID) baseline noise inquiry data foreach event (E1, E2) may also be different. An event E3 may surface frominquiry data related to the City of Cannes, France where the key termsmay reference the Cannes Film Festival. An event E4 may surface keyterms associated with the Sundance Film Festival in locality Park City,Utah. Another event E5 may surface key terms associated with theAustralian Open Tennis Tournament held in locality Melbourne, Australia.

In the nation of Brazil, events E6-E12 may surface key terms associatedwith different World Cup Soccer venues that are held at different citiesin Brazil (e.g., localities such as Rio de Janeiro, Recife, Manaus,Natal, Cuiaba, Porto Alegre, Belo Horizonte, Brasilia, Salvador,Fortaleza, etc.). Each of the events E6-E12 may occur at different adifferent time t and may have different key word densities ρ (WC ID) andBackground (BKN ID) baseline noise inquiry data that is specific to eachlocality and the events occurring there. Events E1-E12 may be processedby different backend systems 450 or the same backend system 450. Backendsystem 450 may be included in a centralized service in communicationwith one or more networked computing devices, networked data stores,networked compute engines, networked servers, networked client devices,networked wireless client devices or other networked computing systemsor devices.

The centralized service may include a vacation rental company, a carrental company, a leasing agency, a real estate agency or other, forexample. Although a listing or property for lease, sale or rent havebeen described above, the present application may include other forms ofproperty (e.g., a res) and/or services, including but not limited topersonal property, real property, transportation, cleaning services,transportation services, health services, personal services, foodservices, restaurant services, rented goods, leased goods, and othergoods and/or services in commerce, just to name a few. In some examples,one or more of the stages of flow 100 may be performed by thecentralized service. In other examples, one or more of the stages offlow 100 may be distributed among a plurality of the backend systems 450and each backend system may communicate (421, 456) data with otherbackend systems and/or the centralized service. Curated research 107and/or customer service 415 may be included in the centralized serviceas a central resource or as a distributed resource (e.g., curatedresearch 107 and/or 415 distributed among different backend services450).

Although the foregoing examples have been described in some detail forpurposes of clarity of understanding, the above-described conceptualtechniques are not limited to the details provided. There are manyalternative ways of implementing the above-described conceptualtechniques. The disclosed examples are illustrative and not restrictive.

What is claimed is:
 1. A computer-implemented method for detectingevents using inquiries, the computer-implemented method comprising:under control of a computing system comprising one or more processorsconfigured to execute specific instructions, accessing, via anapplication programming interface (“API”) of a data store, inquiry datarepresenting inquiries associated with rental property, wherein theinquiry data comprises: a stock term associated with rental property;and non-stock terms, wherein the non-stock terms include at least a keyterm, a date term corresponding to a rental property stay date, and alocality term corresponding to a rental property stay location;accessing, via the API, baseline data representing a baselinemeasurement of a first frequency with which a non-stock term appears inthe inquiries; identifying data representing one or more date terms inthe inquiry data; identifying data representing one or more key terms inthe inquiry data; identifying data representing one or more localityterms in the inquiry data; generating density data representing ameasurement of a second frequency with which a first key term of the oneor more key terms appears in the inquiry data in connection with atleast one of (i) a date term of the one or more date terms, or (ii) alocality term of the one or more locality terms; analyzing the densitydata with respect to the baseline data; determining, based at leastpartly on the second frequency exceeding the first frequency, that afuture event associated with the first key term is likely to occur in alocality corresponding to the locality term on a date corresponding tothe date term; identifying an entity based at least partly on dataassociating the entity with the locality; and transmitting, to theentity, an electronic message that includes data representing the futureevent.
 2. The computer-implemented method of claim 1, whereinidentifying the entity comprises identifying a proprietor of a rentalproperty associated with the locality.
 3. The computer-implementedmethod of claim 1, further comprising performing a semantic analysis ofat least a subset of the inquiry data to identify the one or more dateterms, the one or more key terms, or the one or more locality terms. 4.The computer-implemented method of claim 1, further comprising:determining data representing a key term frequency for the first keyterm; determining data representing an inverse inquiry frequencyassociated with inquiry data that includes the first key term; andcalculating data representing a product of the key term frequency andthe inverse inquiry frequency.
 5. The computer-implemented method ofclaim 4, further comprising applying data representing a key termweighting factor to the product.
 6. The computer-implemented method ofclaim 1, wherein the density data is generated based at least partly onanalysis of: the one or more date terms; the one or more locality terms;or both the one or more date terms and the one or more locality terms.7. The computer-implemented method of claim 1, wherein the one or morekey terms identified in the inquiry data are derived from datarepresenting free text included in the inquiry data.
 8. Thecomputer-implemented method of claim 1, further comprising: determiningthat a location corresponding to a first term in an inquiry of theinquiries associated with rental property is within a threshold distanceof a predetermined locality; and identifying the first term as aninstance of a locality term that corresponds to the predeterminedlocality.
 9. The computer-implemented method of claim 1, furthercomprising: determining the baseline data using a first portion of theinquiry data, wherein the first portion is associated with a first timeperiod, and wherein the first time period includes the datecorresponding to the date term; and determining second baseline datausing a second portion of the inquiry data, wherein the second portionis associated with a second time period, and wherein the second timeperiod does not include the date corresponding to the first date term.10. A system for detecting events using inquiries, the systemcomprising: an inquiries data store storing: inquiry data representinginquiries associated with rental property, wherein the inquiry datacomprises: a stock term associated with rental property; and non-stockterms, wherein the non-stock terms include at least a key term, alocality term corresponding to a rental property stay location, and adate term corresponding to a rental property stay date; and baselinedata representing a baseline measurement of a first frequency with whicha non-stock term appears in the inquiries; wherein the inquiries datastore exposes an application programming interface (“API”) to provideaccess to data in the inquiries data store; and one or more computerprocessors in communication with the inquiries data store and programmedby executable instructions to at least: access, via the API, the inquirydata stored in the inquiries data store; identify one or more dateterms, one or more locality terms, and one or more key terms in theinquiry data; determine a measurement of a second frequency with which afirst key term of the one or more key terms appears in the inquiry datain connection with at least one of a date term of the one or more dateterms or a locality term of the one or more locality terms; analyze themeasurement of the second frequency of the first key term with respectto the baseline measurement of the first frequency of the first keyterm; determine, based at least partly on the second frequency exceedingthe first frequency, that a future event associated with the first keyterm is likely to occur in a locality corresponding to the locality termon a date corresponding to the date term; identify an entity based atleast partly on data associating the entity with the locality; andtransmit, to the entity, an electronic message comprising informationregarding the future event.
 11. The system of claim 10, wherein the oneor more processors are further programmed by the executable instructionsto perform a semantic analysis of the one or more date terms, the one ormore key terms, or the one or more locality terms.
 12. The system ofclaim 10, wherein the one or more processors are further programmed bythe executable instructions to at least: generate data representing akey term frequency for the first key term; generate data representing aninverse inquiry frequency associated with inquiry data that includes thefirst key term; calculate data representing a product of the key termfrequency and the inverse inquiry frequency; and apply data representinga key term weighting factor to the product.
 13. The system of claim 10,wherein to identify the entity, the one or more processors are furtherprogrammed by the executable instructions to at least identify aproprietor of a rental property associated with the locality.
 14. Thesystem of claim 10, wherein the electronic message includes datarepresenting an address of a property owner having property in alocality of the future event.
 15. The system of claim 10, wherein thefirst frequency comprises a frequency with which any non-stock term isexpected to appear in the inquiries.
 16. The system of claim 10, whereinthe date corresponding to the date term comprises one of a range ofdates during which the future event is likely to occur.
 17. The systemof claim 10, wherein the one or more computer processors are programmedby further executable instructions to at least generate search engineoptimization data based at least partly on the future event, and whereinto transmit the electronic message to the entity, the one or morecomputer processors are programmed by further executable instructions toautomatically apply the search engine optimization data to a networkresource associated with a proprietor of a rental property associatedwith the locality.
 18. The system of claim 10, wherein the one or morecomputer processors are programmed by further executable instructions toat least generate an electronic media item based at least partly on thefuture event, and wherein to transmit the electronic message to theentity, the one or more computer processors are programmed by furtherexecutable instructions to automatically publish the electronic mediaitem to a network resource associated with rental property.
 19. Anon-transitory computer readable medium, comprising machine executableinstructions configured to cause a computing system to at least: access,via an application programming interface (“API”), inquiry datarepresenting inquiries associated with rental property; access, via theAPI, baseline data representing a baseline measurement of a firstfrequency with which a background noise term appears in the inquiries;identify data representing one or more date terms in the inquiry data;identify data representing one or more key terms in the inquiry data;identify data representing one or more locality terms in the inquirydata; generate density data representing a measurement of a secondfrequency with which a first key term of the one or more key termsappears in the inquiry data in connection with at least one of a dateterm of the one or more date terms or a locality term of the one or morelocality terms; analyze the density data with respect to the baselinedata; determine, based at least partly on the second frequency exceedingthe first frequency, that a future event associated with the first keyterm is likely to occur in a locality corresponding to the locality termon a date corresponding to the date term; identify an entity based atleast partly on data associating the entity with the locality; andtransmit, to the entity, an electronic message that includes datarepresenting the future event.
 20. The non-transitory computer readablemedium of claim 19, wherein the background noise term is part of a groupof terms that are not present in a set of stock terms associated withrental property, and wherein each background noise term in the group ofterms is expected to occur in inquiries at the first frequency.